The present disclosure relates generally to the field of prognostics, and particularly to a system and method for estimating remaining useful equipment life.
Estimating a remaining life of equipment is known in the art as prognostics. Remaining useful life (RUL) estimates provide valuable information for operation of modern complex equipment. RUL estimates provide decision malting aids that allow operators to change operational characteristics (such as load) which, in turn, may prolong a life of the equipment. RUL estimates also allow planners to account for upcoming maintenance and set in motion a logistics process that supports a smooth transition from faulted to fully functioning equipment. Estimating RUL is not straightforward because, ordinarily, RUL is conditional on future usage conditions, such as load and speed, for example. Examples of equipment that may benefit from the use of RUL estimates are aircraft engines (both military and commercial), medical equipment, and power plants, for example.
The utility of RUL estimates is in inverse proportion to an amount of associated uncertainty. That is, if an estimate has large confidence bounds, the utility of such an estimate becomes small because an operator would have to make decisions to repair components at an otherwise acceptable level of risk.
Several fundamentally different approaches may be employed to estimate RUL. One is to model from first principles the physics of a system as well as a fault propagation for given load and speed conditions. Such a physics-based model must include detailed knowledge of material properties, thermodynamic behavior, etc.
Alternatively, an empirical (also referred to as experience-based) model can be employed wherein data from experiments at known conditions and component damage levels are used to build a model for a fault propagation rate. Such a model relies heavily on performing a reasonably large set of experiments that sufficiently explores the operating space.
The two approaches mentioned for estimating RUL have various advantages and disadvantages. The physics-based model relies on an assumption that a fault mode modeled using a specific geometry, material properties, temperature, load, and speed conditions will be similar to an actual fault mode. Deviation in any of those parameters will likely result in an error that is amplified over time. In contrast, the experience-based model assumes that the data available sufficiently maps the space and that interpolations (and small extrapolations) from that map can accurately estimate the fault rate. As a consequence, the two approaches will likely arrive at different estimates. In addition, their respective uncertainty bounds are different as well.
What is needed, therefore, is a way to provide real-time (or near real-time) information concerning equipment RUL that resolves the differences of the different approaches, resulting in a more accurate and reliable estimate than individual existing processes alone.